Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

TitleData-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.
Publication TypeJournal Article
Year of Publication2023
AuthorsZang C, Zhang Y, Xu J, Bian J, Morozyuk D, Schenck EJ, Khullar D, Nordvig AS, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R
JournalNat Commun
Date Published2023 Apr 07
KeywordsCOVID-19, Electronic Health Records, Humans, Post-Acute COVID-19 Syndrome, Propensity Score, SARS-CoV-2

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.

Alternate JournalNat Commun
PubMed ID37029117
PubMed Central IDPMC10080528
Institute of Artificial Intelligence for Digital Health
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